Offline methods directly segment instances in videos by considering each instance as a sub-volume in the 3D spatio-temporal volume of the input video.
Inspired by DETR [1]}, a transformer-based method VisTR [2]} first considers VIS as a sequence decoding problem.
This idea is also
adopted by IFC [3]}, SeqFormer [4]}, and Mask2Former [5]}.
Recently, the first semi-supervised VIS approach [6]}, which does not require video annotations, is proposed.
So far, existing approaches for VIS can only handle classification, detection, segmentation, and tracking of instances for a maximum of 40 object categories by training their deep models on the current datasets [7]}, [8]}.
Besides, the mainstream approaches rely heavily on annotated videos.
In comparison, SSIS-Track has no reliance on video instance annotations in the training process
and detects foreground objects and their associated shadows without identifying their categories.
